A new model based on fuzzy integral for cancer prediction

Jinfeng Wang; Jiajie Chen; Hui Wang

doi:10.1109/BIBM.2018.8621186

A new model based on fuzzy integral for cancer prediction

Jinfeng Wang, Jiajie Chen, Hui Wang^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Cancer prediction models provide an important approach to assess risk and prognosis by identifying individuals and enabling estimates of the population burden and cost of cancer. Models also may aid in the evaluation of treatments and interventions. A number of statistical and machine learning techniques have been employed to develop various cancer prediction models. Meanwhile, gene selection is very important for cancer classification. We need to deal with high-dimensional gene space and few samples. But the epistasis means that some genes maybe cover or affect other genes. Fuzzy measure can describe the interaction between genes very well. In this article, we proposed one new model based on fuzzy integral with respect to fuzzy measure for cancer prediction with sparse genes. We can obtain a group of combinations of genes with the highest fuzzy measure values. The new method is applied to two cancer data for testifying the performance. Experimental results show that the proposed model has the highest testing accuracy and F-score by comparing with several state-of-the-art methods.

Original language	English
Title of host publication	Proceedings -2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2309-2315
Number of pages	7
ISBN (Electronic)	9781538654880
ISBN (Print)	9781538654897
DOIs	https://doi.org/10.1109/BIBM.2018.8621186
Publication status	Published - 24 Jan 2019
Externally published	Yes
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: 03 Dec 2018 → 06 Dec 2018

Publication series

Name	Proceedings - IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	03/12/2018 → 06/12/2018

Bibliographical note

Funding Information:
This work is supported by the EU Horizon 2020 (No.: 690238), the Technology Planning Project of Guangdong Province (No.: 2017A040406023) and the Technology Planning Project of Guangzho City (No.: 201804010353). *corresponding author

Publisher Copyright:
© 2018 IEEE.

Keywords

Cancer prediction
Fuzzy measure
Gene selection

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BIBM.2018.8621186Licence: Unspecified

Cite this

Wang, J., Chen, J., & Wang, H. (2019). A new model based on fuzzy integral for cancer prediction. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings -2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2309-2315). Article 8621186 (Proceedings - IEEE International Conference on Bioinformatics and Biomedicine (BIBM)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621186

Wang, Jinfeng ; Chen, Jiajie ; Wang, Hui. / A new model based on fuzzy integral for cancer prediction. Proceedings -2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2309-2315 (Proceedings - IEEE International Conference on Bioinformatics and Biomedicine (BIBM)).

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title = "A new model based on fuzzy integral for cancer prediction",

abstract = "Cancer prediction models provide an important approach to assess risk and prognosis by identifying individuals and enabling estimates of the population burden and cost of cancer. Models also may aid in the evaluation of treatments and interventions. A number of statistical and machine learning techniques have been employed to develop various cancer prediction models. Meanwhile, gene selection is very important for cancer classification. We need to deal with high-dimensional gene space and few samples. But the epistasis means that some genes maybe cover or affect other genes. Fuzzy measure can describe the interaction between genes very well. In this article, we proposed one new model based on fuzzy integral with respect to fuzzy measure for cancer prediction with sparse genes. We can obtain a group of combinations of genes with the highest fuzzy measure values. The new method is applied to two cancer data for testifying the performance. Experimental results show that the proposed model has the highest testing accuracy and F-score by comparing with several state-of-the-art methods.",

keywords = "Cancer prediction, Fuzzy measure, Gene selection",

author = "Jinfeng Wang and Jiajie Chen and Hui Wang",

note = "Funding Information: This work is supported by the EU Horizon 2020 (No.: 690238), the Technology Planning Project of Guangdong Province (No.: 2017A040406023) and the Technology Planning Project of Guangzho City (No.: 201804010353). *corresponding author Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

year = "2019",

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day = "24",

doi = "10.1109/BIBM.2018.8621186",

language = "English",

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editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",

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Wang, J, Chen, J & Wang, H 2019, A new model based on fuzzy integral for cancer prediction. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings -2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)., 8621186, Proceedings - IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Institute of Electrical and Electronics Engineers Inc., pp. 2309-2315, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 03/12/2018. https://doi.org/10.1109/BIBM.2018.8621186

A new model based on fuzzy integral for cancer prediction. / Wang, Jinfeng; Chen, Jiajie; Wang, Hui.
Proceedings -2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2309-2315 8621186 (Proceedings - IEEE International Conference on Bioinformatics and Biomedicine (BIBM)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A new model based on fuzzy integral for cancer prediction

AU - Wang, Jinfeng

AU - Chen, Jiajie

AU - Wang, Hui

N1 - Funding Information: This work is supported by the EU Horizon 2020 (No.: 690238), the Technology Planning Project of Guangdong Province (No.: 2017A040406023) and the Technology Planning Project of Guangzho City (No.: 201804010353). *corresponding author Publisher Copyright: © 2018 IEEE.

PY - 2019/1/24

Y1 - 2019/1/24

N2 - Cancer prediction models provide an important approach to assess risk and prognosis by identifying individuals and enabling estimates of the population burden and cost of cancer. Models also may aid in the evaluation of treatments and interventions. A number of statistical and machine learning techniques have been employed to develop various cancer prediction models. Meanwhile, gene selection is very important for cancer classification. We need to deal with high-dimensional gene space and few samples. But the epistasis means that some genes maybe cover or affect other genes. Fuzzy measure can describe the interaction between genes very well. In this article, we proposed one new model based on fuzzy integral with respect to fuzzy measure for cancer prediction with sparse genes. We can obtain a group of combinations of genes with the highest fuzzy measure values. The new method is applied to two cancer data for testifying the performance. Experimental results show that the proposed model has the highest testing accuracy and F-score by comparing with several state-of-the-art methods.

AB - Cancer prediction models provide an important approach to assess risk and prognosis by identifying individuals and enabling estimates of the population burden and cost of cancer. Models also may aid in the evaluation of treatments and interventions. A number of statistical and machine learning techniques have been employed to develop various cancer prediction models. Meanwhile, gene selection is very important for cancer classification. We need to deal with high-dimensional gene space and few samples. But the epistasis means that some genes maybe cover or affect other genes. Fuzzy measure can describe the interaction between genes very well. In this article, we proposed one new model based on fuzzy integral with respect to fuzzy measure for cancer prediction with sparse genes. We can obtain a group of combinations of genes with the highest fuzzy measure values. The new method is applied to two cancer data for testifying the performance. Experimental results show that the proposed model has the highest testing accuracy and F-score by comparing with several state-of-the-art methods.

KW - Cancer prediction

KW - Fuzzy measure

KW - Gene selection

U2 - 10.1109/BIBM.2018.8621186

DO - 10.1109/BIBM.2018.8621186

M3 - Conference contribution

AN - SCOPUS:85062487698

SN - 9781538654897

T3 - Proceedings - IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

SP - 2309

EP - 2315

BT - Proceedings -2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

A2 - Schmidt, Harald

A2 - Griol, David

A2 - Wang, Haiying

A2 - Baumbach, Jan

A2 - Zheng, Huiru

A2 - Callejas, Zoraida

A2 - Hu, Xiaohua

A2 - Dickerson, Julie

A2 - Zhang, Le

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Y2 - 3 December 2018 through 6 December 2018

ER -

Wang J, Chen J, Wang H. A new model based on fuzzy integral for cancer prediction. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings -2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Institute of Electrical and Electronics Engineers Inc. 2019. p. 2309-2315. 8621186. (Proceedings - IEEE International Conference on Bioinformatics and Biomedicine (BIBM)). doi: 10.1109/BIBM.2018.8621186

A new model based on fuzzy integral for cancer prediction

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this